Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches.

Journal Article (Journal Article)

Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

Full Text

Duke Authors

Cited Authors

  • Bent, B; Cho, PJ; Henriquez, M; Wittmann, A; Thacker, C; Feinglos, M; Crowley, MJ; Dunn, JP

Published Date

  • June 2, 2021

Published In

Volume / Issue

  • 4 / 1

Start / End Page

  • 89 -

PubMed ID

  • 34079049

Pubmed Central ID

  • PMC8172541

Electronic International Standard Serial Number (EISSN)

  • 2398-6352

Digital Object Identifier (DOI)

  • 10.1038/s41746-021-00465-w


  • eng

Conference Location

  • England